DECNet: Left Atrial Pulmonary Vein Class Imbalance Classification Network [0.03%]
DECNet：左心房肺静脉分类不平衡校正网络
GuoDong Zhang,WenWen Gu,TingYu Liang et al.
GuoDong Zhang et al.
In clinical practice, the anatomical classification of pulmonary veins plays a crucial role in the preoperative assessment of atrial fibrillation radiofrequency ablation surgery. Accurate classification of pulmonary vein anatomy assists phy...
Lesion Classification by Model-Based Feature Extraction: A Differential Affine Invariant Model of Soft Tissue Elasticity in CT Images [0.03%]
基于模型的特征提取下的病变分类：CT图像下软组织弹性的一种微分仿射不变模型
Weiguo Cao,Marc J Pomeroy,Zhengrong Liang et al.
Weiguo Cao et al.
The elasticity of soft tissues has been widely considered a characteristic property for differentiation of healthy and lesions and, therefore, motivated the development of several elasticity imaging modalities, for example, ultrasound elast...
2023 Industry Perceptions Survey on AI Adoption and Return on Investment [0.03%]
2023年人工智能采纳及投资回报行业认知调查
Mitchell Goldburgh,Michael LaChance,Julia Komissarchik et al.
Mitchell Goldburgh et al.
This SIIM-sponsored 2023 report highlights an industry view on artificial intelligence adoption barriers and success related to diagnostic imaging, life sciences, and contrasts. In general, our 2023 survey indicates that there has been prog...
Confidence-Aware Severity Assessment of Lung Disease from Chest X-Rays Using Deep Neural Network on a Multi-Reader Dataset [0.03%]
基于多阅片员数据集的胸部X光深神经网络自信程度意识性肺部疾病严重性评估
Mohammadreza Zandehshahvar,Marly van Assen,Eun Kim et al.
Mohammadreza Zandehshahvar et al.
In this study, we present a method based on Monte Carlo Dropout (MCD) as Bayesian neural network (BNN) approximation for confidence-aware severity classification of lung diseases in COVID-19 patients using chest X-rays (CXRs). Trained and t...
From Revisions to Insights: Converting Radiology Report Revisions into Actionable Educational Feedback Using Generative AI Models [0.03%]
从修订到见解：使用生成式AI模型将放射学报告修订转换为可操作的教育反馈
Shawn Lyo,Suyash Mohan,Alvand Hassankhani et al.
Shawn Lyo et al.
Expert feedback on trainees' preliminary reports is crucial for radiologic training, but real-time feedback can be challenging due to non-contemporaneous, remote reading and increasing imaging volumes. Trainee report revisions contain valua...
Deep Learning-Based Model for Non-invasive Hemoglobin Estimation via Body Parts Images: A Retrospective Analysis and a Prospective Emergency Department Study [0.03%]
基于深度学习的非侵入式血红蛋白估计模型：回顾性分析与前瞻性急诊科研究
En-Ting Lin,Shao-Chi Lu,An-Sheng Liu et al.
En-Ting Lin et al.
Anemia is a significant global health issue, affecting over a billion people worldwide, according to the World Health Organization. Generally, the gold standard for diagnosing anemia relies on laboratory measurements of hemoglobin. To meet ...
Harnessing Deep Learning for Accurate Pathological Assessment of Brain Tumor Cell Types [0.03%]
利用深度学习准确评估脑肿瘤细胞类型病理学的方法
Chongxuan Tian,Yue Xi,Yuting Ma et al.
Chongxuan Tian et al.
Primary diffuse central nervous system large B-cell lymphoma (CNS-pDLBCL) and high-grade glioma (HGG) often present similarly, clinically and on imaging, making differentiation challenging. This similarity can complicate pathologists' diagn...
Interactive Multi-scale Fusion: Advancing Brain Tumor Detection Through Trans-IMSM Model [0.03%]
交互式多尺度融合：通过Trans-IMSM模型推进脑肿瘤检测技术
Vasanthi Durairaj,Palani Uthirapathy
Vasanthi Durairaj
Multi-modal medical image (MI) fusion assists in generating collaboration images collecting complement features through the distinct images of several conditions. The images help physicians to diagnose disease accurately. Hence, this resear...
Deep Convolutional Neural Network for Automated Staging of Periodontal Bone Loss Severity on Bite-wing Radiographs: An Eigen-CAM Explainability Mapping Approach [0.03%]
基于咬合片的牙周骨丢失严重程度自动分期的深度卷积神经网络：一种Eigen-CAM可解释性映射方法
Mediha Erturk,Muhammet Üsame Öziç,Melek Tassoker
Mediha Erturk
Periodontal disease is a significant global oral health problem. Radiographic staging is critical in determining periodontitis severity and treatment requirements. This study aims to automatically stage periodontal bone loss using a deep le...
Construction and Validation of a General Medical Image Dataset for Pretraining [0.03%]
通用医疗图像预训练模型的数据集构建与验证方法研究
Rongguo Zhang,Chenhao Pei,Ji Shi et al.
Rongguo Zhang et al.
In the field of deep learning for medical image analysis, training models from scratch are often used and sometimes, transfer learning from pretrained parameters on ImageNet models is also adopted. However, there is no universally accepted ...